Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma

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Osteosarcoma (OS) is a differentiation disease caused by the genetic and epigenetic differentiation of mesenchymal stem cells into osteoblasts. OS is a common, highly malignant tumor in children and adolescents. Fifteen to 20 % of the patients find distant metastases at their first visit.

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Nội dung Text: Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma

Zhang et al. BMC Cancer (2021) 21:1285 https://doi.org/10.1186/s12885-021-09011-z RESEARCH Open Access Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma Pei Zhang1, Keteng Xu2*, Jingcheng Wang1,3*, Jiale Zhang3 and Huahong Quan4 Abstract Purpose: Osteosarcoma (OS) is a differentiation disease caused by the genetic and epigenetic differentiation of mes- enchymal stem cells into osteoblasts. OS is a common, highly malignant tumor in children and adolescents. Fifteen to 20 % of the patients find distant metastases at their first visit. The purpose of our study was to identify biomarkers for tracking the prognosis and treatment of OS to improve the survival rate of patients. Materials and methods: In this study, which was based on Therapeutically Applicable Research to Generate Effec- tive Treatments (TARGET), we searched for m6A related lncRNAs in OS. We constructed a network between lncRNA and m6A, and built an OS prognostic risk model. Results: We identified 14,581 lncRNAs by using the dataset from TARGET. We obtained 111 m6A-related lncRNAs through a Pearson correlation analysis. A network was built between lncRNA and m6A genes. Eight m6A-related lncRNAs associated with survival were identified through a univariate Cox analysis. A selection operator (LASSO) Cox regression was used to construct a prognostic risk model with six genes (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) obtained through least absolute shrinkage. We also discovered upregulated levels of DLGAP1-AS2 and m6A methylation in osteosarcoma tissues/cells compared with normal tissues/osteoblasts cells. Conclusion: We constructed a risk score prognosis model of m6A-related lncRNAs (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) using the dataset downloaded from TRAGET. We verified the value of the model by dividing all samples into test groups and training groups. However, the role of m6A-related lncRNAs in osteosarcoma needs to be further researched by cell and in vivo studies. Keywords: Osteosarcoma, N6-methylandenosine, Bioinformatics *Correspondence: xuketeng1994@163.com; sbyywjc@csu.edu.cn 1 Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China 2 Department of Joint surgery, Huangshan City People’s Hospital, Huangshan, Anhui, China Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Zhang et al. BMC Cancer (2021) 21:1285 Page 2 of 13 Background data using mathematical, statistical and computational Osteosarcoma (OS), a disease that mainly affects children methods. Due to the large amount of data generated and adolescents, is a common malignant tumor that occurs by new technologies such as genome sequencing and in the mesenchymal tissue of the metaphysis of long bones microarray chip technology, the traditional gene-by- [1–3]. Although extensive surgical resection combined with gene method is not enough to meet the growth and chemotherapy and radiotherapy has achieved certain good demands of biological research. Therefore, bioinfor- results, about 40–50% of patients experience lung metasta- matics is a valuable way to expand biological insight sis [4, 5]. The five-year survival rate of the patients with lung and promote the development of new therapeutic metastases is only 28%. Thus, it is important to develop a approaches. We searched for m6A -related lncRNAs, novel therapeutic approach to effectively treatOS. based on Therapeutically Applicable Research to Gen- More than 60% of all RNA modifications are RNA erate Effective Treatments (TARGET, https://ocg.can- methylation. N6-methyladenosine (m6A) is the most cer.gov/programs/target). Using bioinformatic and common form of modification of human messenger statistical analysis methods, we constructed a prog- RNA (mRNA) and long non-coding RNA (lncRNA) [6, nostic risk model to identify biomarkers related to OS 7]. M6A modification mainly occurs on the adenine of prognosis and treatment to improve the survival rate of the RRACH sequence, and its function is mainly related patients. to writers, readers, and erasers. Writers (RBM15B, METTL3, METTL14, METTL16, RBM15, WTAP, Methods VIRMA [KIA1499], and ZC3H13) are methyltransferases Data collection and data processing [8, 9]. The eraser is a demethylase that can reverse meth- We downloaded the OS gene expression profiles, and ylation and includes FTO and ALKBH5. The reader is the corresponding clinical data from the TARGET data- signal transducers, with m6A binding protein that can base, which comprised a total of 84 OS samples. LncR- bind to M6A, including FMR1, HNRNPC, HNRNPA2B1, NAs were identified using the Ensemble IDs of the genes. IGFBP1, IGFBP2, IGFBP3, LRPPRC, RBMX, YTHDC1, All lncRNAs were extracted base on the perl. Expression YTHDC2, YTHDF1, YTHDF2, and YTHDF3. Accumu- matrixes of 23 m6A genes were extracted from previous lating evidence has shown that m6A modification plays an publications, including the expression data on writers important role in tumorigenesis and tumor progression, (RBM15B, METTL3, METTL14, METTL16, RBM15, such as hepatocellular carcinoma, lung cancer, and leuke- WTAP, VIRMA [KIA1499], and ZC3H13), readers (FMR1, mia [10–12]. Wang et al. revealed that METTL3 promotes HNRNPC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, LRP- tumorigenesis by up-regulating m6A modification of APC PRC, RBMX, YTHDC1, YTHDC2, YTHDF1, YTHDF2, [13]. Qu et al. found that HBx and ALKBH5 promote and YTHDF3) and erasers (FTO and ALKBH5). hepatocellular carcinogenesis through a positive feedback loop [14]. Cao et al. reported that IGF2BP2 can regulate Identification of m6A‑related lncRNAs insulin sensitivity and glucose metabolism to mediate Using a Pearson correlation analysis, we searched for cancer pathogenesis [11]. Additionally, ALKBH5 has been m6A-related lncRNAs in each gene (with the | Pearson shown to negatively regulate the osteogenic differentia- R| > 0.3 and P
Zhang et al. BMC Cancer (2021) 21:1285 Page 3 of 13 Construction of prognosis risk score model We divided all samples into low-risk groups and high- Utilizing a least absolute shrinkage and selection operator risk groups, using the median risk score as the cutoff value. (LASSO) regression analysis, all m6A-related lncRNAs were To avoid random allocation bias, all OS patients were ran- analyzed to select the most optimal prognostic biomarkers domly grouped at a ratio of 0.5:0.5 into a training cohort and to construct the risk score model. A LASSO regression and a test cohort. Using a KM survival analysis, and cal- analysis is a penalty regression method that reduces over- culating the area under the ROC curve (AUC), and etc., fitting by simultaneously performing shrinkage and model we assessed the accuracy and efficiency of the risk score selection. Some coefficients can be compressed to 0 based model. We performed the univariate and multivariate Cox by constructing a penalty function, resulting in a more survival analyses according to clinical factors such as sex, refined model. Therefore, the superiority of subset shrink- age, primary site of OS and metastasis to assess the predic- age is retained. In addition, the LASSO model can perform tive independence of the risk score model. In addition, KM biased estimation of multicollinearity data processing and survival analysis was applied to confirm the effectiveness realize the selection of variables in the estimation. The ideal of the risk score model for different clinical features. method to solve the multicollinearity problem in a regres- sion analysis is a LASSO regression analysis. RNA m6A quantification The m6A RNA methylation assay kit (ab185912; Abcam, Validation of prognosis risk score model UK) was utilized to measure the m6A content. Briefly, In this study, we calculated the prognosis risk scores extracted RNAs were coated on each well, followed by based on a formula as follows: adding capture antibody and detection antibody. Finally, ∑ the m6A levels were detected by measuring the absorb- Risk score = lncRNAsCox coefficient × lncRNAsexpression levels . ance at 450 nm using a microplate reader. Fig. 1 Study flow chart
Zhang et al. BMC Cancer (2021) 21:1285 Page 4 of 13 RNA isolation and qRT‑PCR defined as an m6A-related lncRNA with an expression Total RNA was isolated using RNeasy Mini Kit (Qia- value was correlated with one or more of the 23 m6A- gen, Germany), and reversely transcribed accord- related genes (| Pearson R| > 0.3 and p
Zhang et al. BMC Cancer (2021) 21:1285 Page 5 of 13 Table 1 The Univariate Cox analysis result of the 8 m6A-related Prognostic analysis and biological functional analysis lncRNAs of the m6A‑related lncRNAs Gene HR HR.95L HR.95H pvalue The KM survival curve was drawn to compare the sur- vival of the two molecular subtypes. In the results, the RP11-799D4.4 0.9981 0.9966 0.9995 0.0087 survival rate of C2 was significantly better than that of C1 RP11-286E11.1 1.0016 1.0000 1.0032 0.0489 (P
Zhang et al. BMC Cancer (2021) 21:1285 Page 6 of 13 Fig. 4 A KM survival curves of C1 and C2. B The heatmap of the relationships between the expression levels of 8 m6A-related lncRNAs and clinicopathological characteristics. C The outcome of KEGG analysis. D The outcome of GO analysis
Zhang et al. BMC Cancer (2021) 21:1285 Page 7 of 13 Fig. 5 A The changing trajectory of each independent variable. B The confidence interval of each lambda Construction of the prognostic risk model KM survival curves (P = 0.002, Fig. 6D). The heatmap Using a LASSO-Cox regression analysis, we further selected showed the high-risk samples and low-risk samples had appropriate m6A-related lncRNAs to maintain a high accu- different m6A-related lncRNA expression levels (Fig. 6E). racy rate. We show the changing trajectory of each inde- The risk score distribution of the test cohort is shown in pendent variable in Fig. 5A. The confidence interval of each Fig. 7A, and Fig. 7B shows the survival time of the test lambda was shown in Fig. 5B. These figures showed that a cohort. The AUC of test cohort was 0.816 (Fig. 7C). The total of six genes were selected as the target genes for sub- test cohort had 17 high-risk samples and 25 low-risk sequent analysis when the model reached the optimal samples.. There was a significant difference between value. The six m6A-related lncRNAs were obtained as the high-risk samples and low-risk samples in the KM sur- most suitable predictors for prognosis (RP11-286E11.1, vival curves (P = 0.003, Fig. 7D). The heatmap shows that LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, the high-risk samples and low-risk samples had different AC002398.11). A risk scoring model to predict the prognosis expression levels of m6A-related lncRNA (Fig. 7E). of OS patients was constructed as follows: RP11-286E11.1 * 0.00097667835205502 + LINC01426 * (− 0.0024589660877 Prognostic analysis of the risk model and clinical features 4788) + AC010127.3 * (0.00475697115852846) + DLGAP1- Using a univariate and multivariate Cox regres- AS2 * (0.00138111340031212) + RP4-657D16.3 * (− 0.004196 sion analysis, we analyzed clinical variables, such 85380105118) + AC002398.11 * (− 0.000637455261090537). as age, gender, site, and metastasis (Fig. 8A, B, C, D). The P-value of the univariate and multivariate Evaluation of the risk model Cox survival analysis in the training group and the We randomly divided the 84 samples into two groups, 42 test groupwas
Zhang et al. BMC Cancer (2021) 21:1285 Page 8 of 13 Fig. 6 A The risk score distribution of the training cohort. B The survival time of the training cohort. C The AUC of the training cohort. D The KM survival curves of the training cohort. E The heatmap of the expression of m6A-related lncRNAs in the high-risk group and low-risk group were high risk and 18 patients as low risk (P = 0.010, was the leg or foot in 77 patients, the arm or hand Fig. 9B). Of the 84 OS patients, there were 37 women in 5 patients, and the pelvis in 2 patients., s. In the and 47 men. Seventeen women were classified into group of patients with the leg as the primary OS the high-risk group and 20 women were classified site, 35 patients s were high risk and 42 patientswere into the low-risk group (P
Zhang et al. BMC Cancer (2021) 21:1285 Page 9 of 13 Fig. 7 A The risk score distribution of the test cohort. B The survival time of the test cohort. C The AUC of the test cohort. D The KM survival curves of the test cohort. E The heatmap of the expression of m6A-related lncRNAs in the high-risk group and low-risk group samples. Although the P value> 0.05, the expression risk score model for prognosis. The application value of level of DLGAP1-AS2 in each pair of tissues was higher the model was tested with ROC curve analysis and KM in the tumor group than in the normal group. survival curves. In addition, a univariate and multivari- ate Cox regression analysis also confirmed the validity of Discussion the model. Our study investigated the prognostic significance of According to GO analysis, the m6A-related lncRNAs m6A-related lncRNAs in 84 OS patients from the TAR- are enriched in histone H3-K27 methylation, pericen- GET dataset. We identified a total of 111 m6A-related tric heterochromatin, and phosphatase activator activ- lncRNA in our study, and eight m6A-related lncRNAs ity. Histone H3 mutations have been found to play a role were selected by conducting a univariate Cox analysis. in a variety of cancers, such as paediatric brain tumors We then performed a KEGG analysis and a GO analysis [22]. Penin et al. reported that pericentric heterochro- according to the cluster assignments.. Six m6A-related matin of chromosome 9 was the primary target of HSF1 lncRNAs (RP11-286E11.1, LINC01426, AC010127.3, in both normal and tumor heat-shocked cells [23]. DLGAP1-AS2, RP4-657D16.3, and AC002398.11) were Zheng et al. found that LINC00174 promotes the metas- identified in a LASSO Cox analysis as the most suitable tasis and growth of OS through upregulating slingshot
Zhang et al. BMC Cancer (2021) 21:1285 Page 10 of 13 Fig. 8 A Univariate Cox regression analyses of the training group. B Multivariate Cox regression analyses of the training group. C Univariate Cox regression analyses of the test group. D Multivariate Cox regression analyses of the test group protein phosphatase 2 expression [24]. A KEGG analy- [30]. Chen et al. reported that WTAP promotes OS sis found that the m6A-related lncRNAs are enriched growth and metastasis by inhibiting HMBOX1 expres- in the mTOR pathway. Mickymaray et al. found that sion [31]. Yuan et al. found that ALKBH5 suppresses rhaponticin can inhibit OS growth by inhibiting the OS progression by inhibiting the pre-miR-181b-1/ PI3K-Akt-mTOR pathway [25]. Liu et al. reported that YAP signaling axis [32]. However, the role of m6A- miR-140 inhibits OS development by influencing ubiq- related lncRNAs in OS is still unclear. We found that uitin-specific protease 22 and promoting p21 expression our risk score model built with six m6A-related lncR- [26]. NAs could accurately predict the prognosis of OS. Liu A number of studies have suggested that m6A modi- et al. found that LINC01426 facilitates the progression fication may play a regulatory role in the development and stemness in lung adenocarcinoma [19]. In addi- of cancer. M6A-related could serve as new therapeu- tion, LINC01426 accelerates glioblastoma progres- tic targets and prognostic biomarkers for OS [27]. sion by regulating miR-345-3p/VAMP8 signaling axis Miao et al. found that METTL3 regulates m6A levels [33]. Liu et al. reported DLGAP1-AS2 is associated of LEF1 and activates the Wnt/β-Catenin signaling with poor prognosis in cholangiocarcinoma [34]. In pathway to promote OS progression [28]. Zhou et al. hepatocellular carcinoma cell, DLGAP1-AS2 knock- demonstrated that METTL3 acts as an oncogene in down inhibits cell metastasis by regulating miR-154-5p OS by regulating ATAD2 [29]. Liu et al. reported that methylation [21]. We also discovered upregulated lev- METTL14 overexpression promotes OS cell apoptosis els of DLGAP1-AS2 and m6A methylation in osteo- and inhibits tumor progression by activating caspase3 sarcoma tissues/cells compared with normal tissues/
Zhang et al. BMC Cancer (2021) 21:1285 Page 11 of 13 Fig. 9 A The KM survival curves of age > 14. B The KM survival curves of age
Zhang et al. BMC Cancer (2021) 21:1285 Page 12 of 13 Fig. 11 QRT-PCR analysis of DLGAP1-AS2 levels in OS tissues and adjacent normal tissues osteoblasts cells. Among the six m6A-related lncRNAs Declarations used to construct the risk prognosis model, several Ethics approval and consent to participate genes have been reported to be associated with tumor Not applicable. prognosis such as respiratory system tumors and digestive system tumors [35, 36]. However, no research Consent for publication Not applicable. has reported the relationship between OS and these genes or how the lncRNAs interact with m6A-related Competing interests genes. Therefore, the aim of our study is to identify The authors declare that they have no competing interests. lncRNAs associated with OS prognosis and provide Author details new targets to prevent poor OS prognosis. A limitation 1 Department of Orthopedics, The Second Xiangya Hospital of Central South is that we have not further researched the role of m6A- University, Changsha 410011, Hunan, China. 2 Department of Joint surgery, Huangshan City People’s Hospital, Huangshan, Anhui, China. 3 Department related lncRNAs in osteosarcoma through cell and of Orthopedics, Clinical Medical College, Yangzhou University, Northern in vivo studies. Using the dataset downloaded from Jiangsu People’s Hospital, Yangzhou, China. 4 Department of Graduate, Dalian TRAGET, we constructed a risk score prognosis model Medical University, Dalian 116044, Liaoning, China. of m6A-related lncRNAs. By dividing all samples into Received: 30 August 2021 Accepted: 15 November 2021 test groups and training groups, we verified the value of the model. However, the role of m6A-related lncR- NAs in osteosarcoma needs to be further researched by cell and in vivo studies. References 1. Wen JF, Jiang YQ, Li C, Dai XK, Wu T, Yin WZ. LncRNA-SARCC sensitizes Acknowledgments osteosarcoma to cisplatin through the miR-143-mediated glycolysis We are sincerely acknowledging the contributions from the TARGET. inhibition by targeting hexokinase 2. Cancer Biomark. 2020;28(2):231–46. 2. Sasaki R, Osaki M, Okada F. Microrna-based diagnosis and treatment of Authors’ contributions metastatic human osteosarcoma. Cancers. 2019;11(4):553. XK and ZP conceived of the design of the study. ZP, QHH, and WJC partici- 3. Shimbo K, Miyaki S, Ishitobi H, Kato Y, Kubo T, Shimose S, et al. Exosome- pated in the study design. XKT and ZJL performed the statistical analysis. XKT formed synthetic microRNA-143 is transferred to osteosarcoma cells and finished the manuscript. The final manuscript was read and approved by all inhibits their migration. Biochem Biophys Res Commun. 2014;445(2):381–7. authors. 4. Hirahata M, Osaki M, Kanda Y, Sugimoto Y, Yoshioka Y, Kosaka N, et al. PAI-1, a target gene of miR-143, regulates invasion and metastasis by Availability of data and materials upregulating MMP-13 expression of human osteosarcoma. Cancer All data are fully available without restriction. In this study, publicly available Med. 2016;5(5):892–902. datasets were analyzed. This data can be found here: Therapeutically Appli- 5. Yu X, Hu L, Li SY, Shen J, Wang DL, Xu RJ, et al. Long non-coding RNA cable Research to Generate Effective Treatments (TARGET, https://ocg.cancer. taurine upregulated gene 1 promotes osteosarcoma cell metastasis by gov/programs/target). mediating HIF-1 alpha via miR-143-5p. Cell Death Dis. 2019;10(4):280.
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Zheng C, Li R, Zheng S, Fang H, Xu M, Zhong L. LINC00174 facilitates cell proliferation, cell migration and tumor growth of osteosarcoma via • thorough peer review by experienced researchers in your field regulating the TGF-β/SMAD signaling pathway and upregulating SSH2 • rapid publication on acceptance expression. Front Mol Biosci. 2021;8:697773. • support for research data, including large and complex data types 25. Mickymaray S, Alfaiz FA, Paramasivam A, Veeraraghavan VP, Periadurai ND, Surapaneni KM, et al. Rhaponticin suppresses osteosarcoma • gold Open Access which fosters wider collaboration and increased citations through the inhibition of PI3K-Akt-mTOR pathway. Saudi J Biol Sci. • maximum visibility for your research: over 100M website views per year 2021;28(7):3641–9. 26. Liu W, Wang D, Liu L, Wang L, Yan M. miR-140 inhibits osteosarcoma At BMC, research is always in progress. progression by impairing USP22-mediated LSD1 stabilization and promoting p21 expression. 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